Artificial Intelligence Techniques for Deep Convolutional U-Shape Network with Jump Attention-Based Vision Transformer for Integrated Sequence Scheduling and Trajectory Planning with Obstacle Avoidance in Wireless Rechargeable Sensor Networks: Trends and

Abstract

Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations inherent in traditional Wireless Sensor Networks (WSNs), enabling sustained and autonomous network operation. However, key challenges such as efficient sequence scheduling, trajectory planning, and obstacle avoidance persist due to dynamic environments and strict energy constraints. Recent advances in Artificial Intelligence (AI), particularly deep learning techniques, have significantly enhanced the performance of these tasks. This paper presents a comprehensive review of Deep Convolutional U-Shape Networks (U-Net) integrated with Jump Attention-based Vision Transformers (ViTs) for intelligent WRSN management. U-Net architectures enable effective spatial feature extraction, while transformer models capture long-range dependencies through attention mechanisms, improving trajectory prediction accuracy. Hybrid models such as TransUNet and Swin-based U-Net further enhance spatial understanding by combining convolutional and attention-based approaches. Additionally, reinforcement learning and optimization techniques contribute to adaptive scheduling and efficient trajectory planning. Despite these developments, challenges such as high computational complexity, data dependency, scalability, and real-time deployment limitations remain. This review analyses recent studies (2020–2023), identifies research gaps, and highlights future directions, emphasizing lightweight and edge-based intelligent solutions.